Enhancing Cognitive Robotics with Commonsense through LLM-Generated Preconditions and Subgoals

TL;DR

This paper integrates large language models with symbolic planning to generate commonsense preconditions and subgoals, significantly improving robot task success rates and adaptability in realistic scenarios.

Contribution

It introduces a novel approach combining LLMs with symbolic planning to automatically generate necessary preconditions and subgoals for robotic tasks.

Findings

Higher task success rate compared to baseline

More valid plans generated with LLM assistance

Better adaptation to environmental changes

Abstract

Robots often fail at everyday tasks because instructions skip commonsense details like hidden preconditions and small subgoals. Traditional symbolic planners need these details to be written explicitly, which is time consuming and often incomplete. In this project we combine a Large Language Model with symbolic planning. Given a natural language task, the LLM suggests plausible preconditions and subgoals. We translate these suggestions into a formal planning model and execute the resulting plan in simulation. Compared to a baseline planner without the LLM step, our system produces more valid plans, achieves a higher task success rate, and adapts better when the environment changes. These results suggest that adding LLM commonsense to classical planning can make robot behavior in realistic scenarios more reliable.